{"id":"W6920171737","doi":"10.6068/dp14ba8ac17b730","title":"Trend 2001 - 2011. Statistics Canada. CANSIM: Construction - Nonresidential Building Construction | Country: Canada | Table: Capital expenditures on construction, by type of asset and North American Industry Classification System (NAICS) sector | Variable: Passenger terminals (x 1,000,000), Agriculture, forestry, fishing and hunting | Units: $CAD, 2001-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-035.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Census; Official statistics; Stock (firearms); Asset (computer security); Summary statistics; Descriptive statistics; Capital (architecture)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001411831,0.002304552,0.002302503,0.007972027,0.002850926,0.004492142,0.004342864,0.001331069,0.08040886],"category_scores_gemma":[0.01275232,0.001630962,0.00178417,0.03598268,0.0005432133,0.002244509,0.001938636,0.002663155,0.04776677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04712025,"about_ca_system_score_gemma":0.1083873,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9939797,"about_ca_topic_score_gemma":0.9931347,"domain_scores_codex":[0.9965963,0.0001700919,0.0003292962,0.0004367798,0.001678263,0.0007892853],"domain_scores_gemma":[0.9732838,0.0008437201,0.0008849792,0.000678749,0.02310464,0.001204135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002222974,0.000007100174,0.001349599,0.0002239914,0.00001957609,0.000007651573,0.00002075473,0.0001409599,0.000008879789,0.0003725467,0.9961548,0.001671802],"study_design_scores_gemma":[0.0001232289,0.00001197309,0.02882911,0.0006772639,0.00006081915,0.00002738517,0.0004546941,0.0005696255,0.0001895974,0.0005223493,0.9684605,0.00007360872],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007602187,0.00004920676,0.0000217669,0.0001022975,0.00002097149,0.00001112229,0.9987229,0.00005025985,0.0009454445],"genre_scores_gemma":[0.0009521354,0.0002745051,0.0002793848,0.0001099829,0.00001487212,0.0000833516,0.9937417,0.00008579293,0.004458336],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08040886,"threshold_uncertainty_score":0.3418829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02743653873368403,"score_gpt":0.2519334547867141,"score_spread":0.2244969160530301,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}